Technical Field
This disclosure relates to the field of Optical Coherence Tomography (OCT). This disclosure particularly relates to an OCT system with improved motion contrast. This disclosure particularly relates to motion contrast methods for such OCT systems.
Description of Related Art
Optical coherence tomography (OCT) has become an indispensable clinical imaging tool, since its introduction in 1991. For a background of OCT technology, see, for example, Drexler and Fujimoto et al. “Optical Coherence Technology: Technology and Applications” Springer, Heidelberg, Germany, 2008. This book is incorporated herein by reference in its entirety. OCT is based on an optical measurement technique known as low-coherence interferometry. OCT performs high resolution, cross-sectional imaging of internal microstructure of a physical object by directing a light beam to the physical object, and then measuring and analyzing magnitude and time delay of backscattered light.
A cross-sectional image is generated by performing multiple axial measurements of time delay (axial scans or A-scans) and scanning the incident optical beam transversely. This produces a two-dimensional data set of A-scans (i.e. B-scans), which represents the optical backscattering in a cross-sectional plane through the physical object. Three-dimensional, volumetric data sets can be generated by acquiring sequential cross-sectional images by scanning the incident optical beam in a raster pattern (three-dimensional OCT or 3D-OCT). This technique yields internal microstructural images of the physical objects with very fine details. For example, pathology of a tissue can effectively be imaged in situ and in real time with resolutions smaller than 15 micrometers.
Several types of OCT systems and methods have been developed, for example, Time-domain OCT (TD-OCT) and Fourier-domain OCT (FD-OCT). Use of FD-OCT enables high-resolution imaging of retinal morphology that is nearly comparable to histologic analysis. Examples of FD-OCT technologies include Spectral-domain OCT (SD-OCT) and Swept-source OCT (SS-OCT).
OCT may be used for identification of common retinovascular diseases, such as age-related macular degeneration (AMD), diabetic retinopathy (DR), and retinovascular occlusions. However, despite the rapid evolution of OCT imaging, current OCT technology may not provide adequate visualization of retinal and choroidal microvasculature. Thus, clinicians are often compelled to order both OCT and fluorescein angiography (FA) in patients with the retinovascular diseases. There has been increased interest in using data generated during FD-OCT imaging to generate angiographic images of the fundus. These angiograms are implemented noninvasively without injection of fluorescent dye.
Recently, phase-variance OCT (PV-OCT) has been introduced to image retinal microvasculature. See, for example, Fingler et al. “Dynamic Motion Contrast and Transverse Flow Estimation Using Optical Coherence Tomography” U.S. Pat. No. 7,995,814; Fingler et al. “Dynamic Motion Contrast and Transverse Flow Estimation Using Optical Coherence Tomography” U.S. Pat. No. 8,369,594; Fingler et al. “Mobility and transverse flow visualization using phase variance contrast with spectral domain optical coherence tomography” Opt. Express 2007; 15:12636-53; Fingler et al. “Phase-contrast OCT imaging of transverse flows in the mouse retina and choroid” Invest Ophthalmol. Vis. Sci. 2008; 49:5055-9; Fingler et al. “Volumetric microvascular imaging of human retina using optical coherence tomography with a novel motion contrast technique” Opt. Express 2009; 17:22190-200; Kim et al. “In vivo volumetric imaging of human retinal circulation with phase-variance optical coherence tomography” Biomed Opt Express [serial online] 2011; 2:1504-13; Kim et al. “Noninvasive imaging of the foveal avascular zone with high-speed, phase-variance optical coherence tomography” Invest. Ophthalmol. Vis. Sci. 2012; 53:85-92; and Kim et al. “Optical imaging of the chorioretinal vasculature in the living human eye” PNAS, Aug. 27, 2013, vol. 110, no. 35, 14354-14359. All these publications and patent disclosures are incorporated herein by reference in their entirety.
PV-OCT uses software processing of data normally acquired, but not used, during FD-OCT imaging. With a different scanning protocol than found in commercial instruments, PV-OCT identifies regions of motion between consecutive B-scans that are contrasted with less mobile regions. In the retina and choroid, the regions with motion correspond to the vasculature; these vessels are readily differentiated from other retinal tissues that are relatively static.
An alternative method to acquire images of the retinal vasculature is Doppler OCT, which measures the change in scatterer position between successive depth scans and uses this information to calculate the flow component parallel to the imaging direction (called axial flow). Doppler OCT has been used to image large axial flow in the retina, but without dedicated scanning protocols this technique is limited in cases of slow flow or flow oriented transverse to the imaging direction. Because this technique depends on measuring motion changes between successive depth scans, as imaging speed improvements continue for FD-OCT systems, the scatterers have less time to move between measurements and the slowest motions become obscured by noise. This further reduces the visualization capabilities of typical Doppler OCT techniques.
In contrast, PV-OCT will be able to achieve the same time separations between phase measurements with increased FD-OCT imaging speeds, maintaining the demonstrated ability to visualize fast blood vessel and slow microvascular flow independently of vessel orientation.
Several groups in recent years have developed OCT imaging methods to push beyond conventional Doppler OCT imaging limitations. Some approaches involve increasing the flow contrast through hardware modifications of FD-OCT machines, such as in 2-beam scanning, or producing a heterodyne frequency for extracting flow components. Other investigators have used nonconventional scanning patterns or repeated B-scan acquisitions, such as used in PV-OCT to increase the time separation between phase measurements and enhance Doppler flow contrast of microvascular flow. In addition to phase-based contrast techniques to visualize vasculature, intensity-based visualization of microvasculature has been developed for OCT using segmentation, speckle-based temporal changes, decorrelation-based techniques, and contrast based on both phase and intensity changes. Each of these methods has varying capabilities in regard to microvascular visualization, noise levels, and artifacts while imaging retinal tissues undergoing typical motion during acquisition. Some of the noise and artifact limitations can be overcome with selective segmentation of the volumetric data or increased statistics through longer imaging times, but further analysis is required to be able to compare all of the visualization capabilities from all these different systems.
For further description of OCT methods and systems, and their applications, for example, see: Schwartz et al. “Phase-Variance Optical Coherence Tomography: A Technique for Noninvasive Angiography” American Academy of Ophthalmology, Volume 121, Issue 1, January 2014, Pages 180-187; Sharma et al. “Data Acquisition Methods for Reduced Motion Artifacts and Applications in OCT Angiography” U.S. Pat. No. 8,857,988; Narasimha-Iyer et al. “Systems and Methods for Improved Acquisition of Ophthalmic Optical Coherence Tomography Data” U.S. Patent Application Publication No. 2014/0268046; Everett “Methods for Mapping Tissue With Optical Coherence Tomography Data” U.S. Pat. No. 7,768,652. All these publications and patent disclosures are incorporated herein by reference in their entirety.